Generally, in semiconductor processing, metrology is the measuring of dimensions and characteristics of a semiconductor wafer after processing. Virtual metrology (VM) typically uses a model to predict the resulting dimensions and characteristics of a wafer based on parameters from the processing chamber.
Adaptive VM generally requires building a model based on metrology results from two or three wafers of a lot of wafers and chamber parameters for each tool. This model is then used to predict the outcome of the processing of the wafers of the following lot. Because the control is adjusted on a per lot basis, the control is considered to be lot-to-lot (LtL) control.
However, metrology results for the two or three wafers generally takes between a few hours and half a day to receive from the metrology tools. This delay is typically impracticable for current manufacturing needs. Ideally, VM could be used to control processing on a wafer-to-wafer (WtW) basis to gain higher precision and accuracy were it not for the delay in obtaining metrology results. Also, the conventional methods generally cannot accomplish WtW control because the sampling rate is limited. Further, adaptive VM is not able to identify and correct chamber drift. Thus, conventional adaptive VM methods cannot realize WtW control or correction of chamber drift. Accordingly, there is a need in the art for a method to overcome the above described shortcomings.